International Forum of Educational Technology & Society, National Taiwan Normal
University, Taiwan
Data Theatre as an Entry Point to Data Literacy
Author(s): Rahul Bhargava, Amanda Brea, Victoria Palacin, Laura Perovich and Jesse Hinson
Source:
Educational Technology & Society
, October 2022, Vol. 25, No. 4 (October 2022), pp.
93-108
Published by: International Forum of Educational Technology & Society, National Taiwan
Normal University, Taiwan
Stable URL: https://www.jstor.org/stable/10.2307/48695984
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide
range of content in a trusted digital archive. We use information technology and tools to increase productivity and
facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.
Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at
https://about.jstor.org/terms
International Forum of Educational Technology & Society, National Taiwan Normal University,
Taiwan
is collaborating with JSTOR to digitize, preserve and extend access to
Educational
Technology & Society
This content downloaded from
202.120.224.68 on Wed, 29 Nov 2023 02:34:33 +00:00
All use subject to https://about.jstor.org/terms
Bhargava, R., Brea, A., Palacin, V., Perovich, L., & Hinson, J. (2022). Data Theatre as an Entry Point to Data Literacy.
Educational Technology & Society, 25 (4), 93-108.
93
ISSN 1436-4522 (online) and 1176-3647 (print). This article of the journal of Educational Technology & Society is available under Creative Commons CC-BY-NC-ND
3.0 license (https://creativecommons.org/licenses/by-nc-nd/3.0/). For further queries, please contact Journal Editors at ets.editors@gmail.com.
Data Theatre as an Entry Point to Data Literacy
Rahul Bhargava
1*
, Amanda Brea
1
, Victoria Palacin
2
, Laura Perovich
1
and Jesse Hinson
1
1
Northeastern University, USA //
2
Univerisity of Helsinki, Finland // r.bhargava@northeastern.edu //
brea.am@northeastern.edu // victoria.palacin@helsinki.fi // l.perovich@northeastern.edu //
j.hinson@northeastern.edu
*
Corresponding author
ABSTRACT: Data literacy is a growing area of focus across multiple disciplines in higher education. The
dominant forms of introduction focus on computational toolchains and statistical ways of knowing. As data
driven decision-making becomes more central to democratic processes, a larger group of learners must be
engaged in order to ensure they have a seat at the table in civic settings. This requires a rethinking to support
many paths into data literacy. In this paper we introduce data theatre,” a set of activities designed for data
novices that may have limited experience or comfort with spreadsheets, math, and other quantitative operations.
Through iterative co-design over three workshops, we tested and produced two activity guides for educators,
building on long-standing practices in participatory theatre that center social justice and liberation. Our initial
findings provide very early evidence that this approach can help these learners overcome hesitations to working
with information, begin building a critical perspective when viewing data, and create emotionally impactful data
stories told through theatrical performance. This prototype work suggests to us that the concept of Data theatre
warrants further study to build a more robust understanding of its affordances and limitations.
Keywords: Data literacy, Participatory theatre, Education, Social justice
1. Introduction
Data is shaping our lives and culture in a growing number of ways, from enabling our everyday digital activities,
to supporting scientific breakthroughs, to guiding decision-making at local and international scales. Yet
significant work has documented the negative impacts of this trend towards datafication (Datafication is a
technological trend that seeks to capture large proportions of human activity and turn them into digital and
quantitative data for processing (Couldry & Mejias 2019)), such as racialized outcomes in facial-detection based
policing, embedded gender bias in automated hiring, racial disparities in algorithm-based healthcare diagnosis,
and more (Eubanks, 2018; Noble, 2018; ONeil, 2016). In response to these examples, broader conversations
have begun to integrate questions of justice and equity in data-centered projects (DIgnazio & Klein, 2020;
Raffaghelli, 2020; Williams, 2020).
In parallel, data literacy has grown as a major topic in educational settings across technical and socio-political
domains (Prado & Marzal, 2013; Klenke et al., 2020; Ryan et al., 2019; Timmermann & Havemann, 2020). We
utilize a four-part definition for data literacy,” engaging components related to acquiring data, processing and
analyzing data, representing data, and storytelling with data for some purpose (Bhargava & DIgnazio, 2015).
Regions in Rwanda (UNICEF Rwanda, 2016) and Australia (Data Literacy,” n.d.) have already introduced data
literacy as part of their digital literacy curriculums, and the European Union has classified data literacy as an
essential skill for the future and since 2016 has implemented policies and initiatives to support its development
(Carretero et al., 2017). Typical introductions to data literacy at the university level follow patterns that have
long existed, privileging those with math and technology fluency and thus reinforcing existing power
asymmetries in data via their structure and application (Bourdieu & Passeron, 1990). These parallel trends
expose a growing need for educational data literacy practices that center principles of justice, emancipation, and
participation.
Motivated by this need, in this paper we build on historical theories of education and embodiment and
participatory theatre to introduce our collaboratively designed data theatre workshop. The first workshop
activity, entitled Embody a Dataset,” invites participants to choreograph a representation of data using the other
participants bodies. The second, entitled Make a (Data) Scene,” asks teams of participants to find a story in a
dataset and quickly design and perform a short scene to tell that story. We introduce motivations, summarize
inspirations and related work, review the design process, document the activities, and discuss initial findings.
This offers an initial case study of introducing data literacy centered around concepts of social justice and
complements our larger body of work building arts-based invitations for data literacy learners.
This content downloaded from
202.120.224.68 on Wed, 29 Nov 2023 02:34:33 +00:00
All use subject to https://about.jstor.org/terms
94
1.1. The need for new paths into data literacy
Justice-informed pieces of building data literacy can look radically different than dominant current approaches.
An important question to engage is precisely what justice means in our work. At a high level we embrace
terms like justice,” equity,” and capacity to align with traditional struggles to retake power by those without.
We take social justice to be a phrase defining a system where members of society can participate fully in their
roles, achieve social mobility, and access collective support structures provided by their communities. We
respect and engage the phrases long history in Western religious thinking, and its connections to more modern
movements such as Liberation Theology (Gutierrez, 1988), but do not choose to center those roots in our
application.
Taking approaches built on this definition is becoming critical as more decision-making in civic settings has
become data-driven. Those who do not speak the language of data do not have social agency in this setting; we
define that as an emerging problem for democratic society. How can those without a seat at the table in civic
decision-making contexts that center data be authentically invited to participate? What barriers are holding them
back? This work is targeted at developing alternate paths into this set of skills to create new ways for all people
to learn to speak data so they can engage in society critically. We are informed by a conception of three main
barriers: (1) focus on statistics (2) centering of technologies (3) control over impact.
First, we find that statistical analytic processes are the primary goal of many introductions to data. Browsing a
mix of educational and professional training, one finds statistics and computation are most often front and center,
introduced without significant discussion of context and ethics (Oliver & McNeil, 2021). This pattern follows a
long arc of abstract mathematical thinking being centered in American educational curricular guidelines
(Scheaffer & Jacobbe, 2014). The current manifestation of this history is the STEM approach to curricular
priorities, proposed as a response to forecasted economic workforce needs (National Academies, 2007). There is
a history of robust critique of the form and intent of US math education (Harouni, 2015). This has led to many
learners forming identities as math or non-math people; definitions only somewhat related to their actual
competence (Leonard et al., 2020; Cribbs et al., 2015). As data science was introduced and claimed by the field
of statistics (Gould, 2017), quantitative analytical approaches became the standard at the expense of creating a
more inclusive set of pathways into the field of working with data. Any introduction to data framed through a
mathematics lens immediately raises a massive barrier to the group identifying as non-math people.
Second, introductions to data literacy in learning settings typically rely on a techno-centric understanding of
these skills, focused on quantitative data stored and analyzed via computational means. Digitization has radically
altered the costs of collecting, storing, and retrieving data of any type, placing computation centrally within any
data project. Related Big Data skills have been in high demand for more than a decade (Manyika et al., 2011).
Focusing on software that supports algorithmic operations on large datasets privileges those with access to
computational tools, which differs across social, economic, and racial divides (Jackson et al., 2008). This
privileges those with power and access. In addition, Small Data continues to play a large role in social contexts
(Blair et al., 2014), despite the hype behind its Big-ger cousin (boyd & Crawford, 2012)
Third, datasets are too often separate from their potential applications and context of influence. Our prior work
has critiqued data projects for their persistent lack of transparency and reliance on extractive collected data
(DIgnazio & Bhargava, 2015). The phrase data is the new oil is quite emblematic of the promise and peril of
this approach (Flender, 2019). There is a relevant parallel here - just as with oil, the benefits of data projects are
gleaned by those in power while the perils are experienced by those typically marginalized. This can be
attributed, in part, to the fact that most data are extracted from a population or setting of study and taken to
another context to be analyzed and interrogated. The analysis and impact conversations rarely integrate the
subjects represented in the data, nor the communities that data-driven decisions will impact.
Prior work has begun to address this. Qualitative data can be introduced alongside quantitative to introduce non-
computational analytical approaches (Henderson & Corry, 2020). Introductions can include concrete examples of
alternate modes of data analysis such as collaborative meaning-making in urban planning processes (Boston
Transportation Department, 2017), or story-finding and storytelling in the design of clothing and traditional
fabrics (Katuli, 2019; Perovich et al., 2020). These examples show how discussion, aesthetics, design, and object
construction (Willett et al., 2017) are forms of data analysis and storytelling. In addition, they demonstrate
community ownership of data analysis, addressing our third main critique.
This content downloaded from
202.120.224.68 on Wed, 29 Nov 2023 02:34:33 +00:00
All use subject to https://about.jstor.org/terms
95
1.2. An arts-based data-theatre approach: Research questions
In this paper, we explore theatre, an arts-method, as a promising approach to address the broader need for new
paths into speaking the language of data. Art-based methods have long been used to enhance learning in diverse
disciplines, such as medicine (de la Croix et al., 2011), math (Hally & Sinha, 2018), and physics (Solomon et al.,
2021). These methods offer learners an opportunity to tap into their kinesthetic intelligence by using their body
to express or understand ideas, concepts, and experiences (Lowenfield, 1957). This may appeal to learners that
embraced performance-based media for sharing information, including data-based facts (such as Tiktok and
Instagram).
We describe the iterative design process, and initial findings, from a series of data theatre workshops we led
online and in person. Our design process finds ample overlaps between the world of participatory theatre, which
has a traditional focus on social justice, and our arts-based approaches to data literacy. The intersection between
data literacy, justice, and the barriers enumerated above, leads us to our driving motivation: exploring how
participatory theatre can help us build novel introductions to data that center on justice. Specifically, we
formulated three central research questions:
RQ1 - Literacy: In what ways can theatrical activities introduce learners to data literacy skills?
RQ2 - Critique: How might embodying data help lead to a critical questioning of datasets and their use?
RQ3 - Impact: Does the process of performing a data story change performer or audience perspectives on
the subjects of the data?
We engage these questions in our workshop design and throughout this paper. At the same time, this early-stage
work is still speculative and exploratory as we work with learners to build a practice of data theatre, developing a
novel approach as a basis for further study. Future work will explore ways to more robustly assess causal
relationships between workshops and data literacy outcomes, relative to more traditional data learning activities.
2. Related work
In broaching the questions of what data theatre might look like, what justice-oriented learning goals it might
fulfill, and its potential for impact, we build on significant work in three areas: (1) education and embodiment,
(2) participatory theatre practices (3) professional theatre practice.
This is guided by central design principles established in our previous work (Bhargava & DIgnazio, 2015):
Participation from all parties
Learner-guided explorations
Facilitation over teaching
Accessibility to a diverse set of learners
Focus on real problems in the community
In prior projects these principles have guided the development of learning experiences to design and paint
community data murals (Bhargava et al., 2016) and build physical data sculptures with crafts materials
(Bhargava & DIgnazio, 2017). This new foray into activities for data theatre introduces our work to a rich
history of education through embodiment, and introduces a new performative output for the learning activities
we create.
2.1. Education and embodiment
Research in education and embodiment surfaces the importance of emancipation through critical data literacies
and points to the ways in which physical movement and action may alter understanding, perception, and
memory.
Many pedagogical approaches have embraced justice and equity at their core. We take strong inspiration from
Deweys foundational connection between education and democracy (Dewey, 1903) and Freires approach to
emancipatory education (Freire, 1968). In the context of data literacy, we specifically posit that the application of
this idea to modern day involves both understanding the oppression of data processes integrated into civic
governance and taking back power to use data in service of collective justice (Couldry & Mejias 2019).
Functionally, this builds on research into how learning can be more effective when the subject is interrogating
issues that matter in their communities (Zeidler et al., 2005). From this work we draw an approach that works
This content downloaded from
202.120.224.68 on Wed, 29 Nov 2023 02:34:33 +00:00
All use subject to https://about.jstor.org/terms
96
with local community data, provides creative freedom to find narratives in it, and focuses on potential real-world
impact of the process.
An emerging definition of critical data literacy applies Freires approach to the concept of data literacy (Tygel
& Kirsch, 2015; Sander, 2020). This conceptualization offers links to previous movements in critical media
literacy and digital literacy (Knaus, 2020). It also provides a theoretical framework to underpin the design of
activities and interventions for educational contexts that seek to raise awareness of the datafied society around us
and put data in the hands of the communities it is too often extracted from. Critical data literacy embeds a hope
for empowerment through activism and social change (Gutiérrez, 2018). The approach of critical data literacy
offers us theoretical framing within which to situate our activities, and a set of related projects that our work
connects to.
Finally, we find inspiration in embodied approaches to learning. These build on understandings of learning as
situated in social context, activity, norms, experience, and physicality (Lakoff & Johnson, 1999). Indeed,
scholars have acknowledged the power of embodied experiences to shape learning (Höök, 2018) and promote
empathy (Yap, 2016). Going further, we embrace the idea that all forms of knowing are rooted in the somatic
phenomenon of the learner - the physical experiences they undergo and create while in learning settings
(Abrahamson & Lindgren, 2014; Malinverni & Pares, 2014). Data science educators have built on this type of
definition to offer activities such as bodily data sorting and physical infographics (Sommer & Polman, 2017).
The latter two are examples of participatory simulations - opportunities to socially construct an understanding of
phenomenon by embodying it. In this practice the act of knowing itself is participatory and shapes outcomes
(Barab et al., 1999). The emerging concept of data feminism embraces this, suggesting data embodiment and
data visceralization be valued as ways of knowing data (DIgnazio & Klein, 2020).
Furthermore, our definition of justice comes together with a motivation in an attempt to embrace
epistemological pluralism in the domain of data science. As defined by Turkle and Papert, epistemological
pluralism is an acceptance of multiple ways of knowing and thinking,” with each equally valid on its own
terms (Turkle & Papert, 2015). Their conceptualization connects to gender rights in learning settings, respect
for previously dismissed concretized learning, and notions of body syntonicity. We are inspired and motivated to
create a data science that engages and values epistemological pluralism, moving beyond a belief that algorithmic
and formal thinking is naturally superior. Embodied approaches to building data literacy are one piece of this
puzzle.
2.2. Participatory theatre
Our work draws on the long-standing focus on liberation in participatory theatre and the many practical activities
built by expert practitioners in this space over decades of contextual work with communities.
The approach of epic theatre is a common root for many of these practices, built around the idea that theatrical
practices can provoke the audience to reflect on the world as it is, rather than suspending disbelief and entering
an imagined world via the performance (Styan, 1981). Theatre of the Oppressed, a form of theatre designed for
social and political activism, explores oppression in all forms - internal / external, individual / societal, and more
(Boal, 1993). Verbatim theatre (Catchesides, 2020), and image theatre (Farmer, 2014) are all examples of other
approaches that have grown from this common theoretical core and been successfully used for empowerment in
communities. Playback theatre, where participants share experiences which are then played back to the broader
group through, has been used to promote compassion and understanding in medical education (Salas et al.,
2013), healing and reconciliation for post-war communities (Dirnstorfer & Saud, 2020), mental-health recovery
(Moran & Alon, 2011), and healing for adolescents in refugee camps (Edelbi, 2020). It aims to create a deeper,
multi-dimensional understanding of experiences building empowerment and engagement in decision-making.
These approaches interrupt the theatrical experience and charge participants with decision making, analysis, and
sharing judgment, blurring the lines between actors and spectators. The audience may have strong visceral and
emotional reactions to a piece, but they are more likely to take social action when they are invited to act in the
experience of the theatre. Functionally, these approaches work in support of critical thinking and participatory
engagement via what Brecht refers to as a dialectical approach, focused on meaning making through
engagement of potentially opposing perspectives (Mumford, 2008).
These forms of theatre align with our goal to create participatory spaces that bring people together to learn how
to speak and embody data, and leverage this knowledge towards goals within their communities. The element of
shared discovery and storytelling, structurally influenced by the perspectives and experiences of participants, can
This content downloaded from
202.120.224.68 on Wed, 29 Nov 2023 02:34:33 +00:00
All use subject to https://about.jstor.org/terms
97
create a sense of control and responsibility amongst the collaborators (PARCOS, 2020). In the words of social
science dramatists and researchers Ross Gray and Christina Sindig, A drama that emerges from such a process
is very much a negotiated settlement, a collective achievement (Gray & Sinding, 2002). Participant audiences
reconcile with their experiences through story building, a process that connects strongly to the definition of
critical data literacy.
2.3. Professional performances
Finally, we looked to professional theatre practice for inspirations of performative data productions in action.
The 2015 piece A Sort of Joy (Thousands of Exhausted Things), created by the theatre troupe Elevator Repair
Service and the data visualization group Office of Creative Research (Thorp, 2015) uses metadata from the New
York City Museum of Modern Arts collection to create a performance that highlights the gender of artists whose
pieces are in the collection. Performed at the MoMA itself, the piece centers around faux visitors reading out
names in cadence. A circle of men stands with three women apart. The men speak other male names for several
minutes, until finally the women speak for the first time - saying Mary to highlight the gender gap in museum
acquisitions. The entire structure of the piece pulls the audience into the act of questioning the gender diversity
of the MoMAs collection though experiencing the gaps in the representation via their eyes, ears, and body as it
awaits the next time the women speak.
To further explore and understand professional practice, we conducted two interviews with professionals in the
field of theatre. Our first interview was with Christopher Ellinger, Founder and Director of True Story Theater, a
nonprofit playback theatre company that offers improvisational performances and workshops to community
groups, businesses, and individuals. Ellinger shared a wealth of experiences around how to help participants
develop self-awareness and promote empowerment, reconciliation, and decision-making. Our second interview
was with Frederica Fragapane, an award-winning Italian information designer, who researches the mutualistic
relationship between data visualization and performance. Some of her work has integrated traditional practices
with projected data visualizations that reflect what is happening in the play - such as network diagrams of
character interactions projected behind an unfolding scene (Fragapane, 2017). Her work treats theatrical
production as an artifact for interpretation and representation via standard data visualization approaches; an
alternate conception of what data theatre could be.
3. Workshop and activity design
With this theoretical grounding, and related work across disciplines, we used an iterative process to develop a set
of data theatre activities centered on justice. Over three workshops we iterated on the design of the activities with
students and qualitatively assessed the experiences against our research questions and motivations. Each
workshop focused on one of our driving research questions, while touching on all of them. Our workshops are
also informed by interviews with two professionals in the field of participatory theatre. In this section we
summarize our methods, workshops, and observations.
3.1. Workshop structure and development
The three workshops included (1) an initial exploratory workshop focused on RQ1- Literacy, (2) a prototype
theatre-focused workshop focused on RQ2 - Critique, and (3) a single session in-class workshop focused on RQ3
- Impact (Table 1).
Table 1. Workshop format and details
Workshop #1
Workshop #2
Workshop #3
Exploring Movement
May 2019
Prototype Data Theatre
December 2020
In-Class Data Theatre
February 2021
In-person
Graduate students
Virtual (via Zoom)
Theatre undergraduates
In-person
Theatre undergraduates
15 participants
Self-selected
Focused on RQ1 - Literacy
10 participants
Self-selected
Focused on RQ1 - Critique
12 participants
Class members
Focused on RQ3 - Impact
Each workshop was built around an introductory experience to set tone and activate participants bodies,
followed by two in-depth activities. Workshops were 1.5 hours, with interspersed reflection discussions between
This content downloaded from
202.120.224.68 on Wed, 29 Nov 2023 02:34:33 +00:00
All use subject to https://about.jstor.org/terms
98
activities. Discussions with participants during the sessions were facilitated as semi-structured focus groups,
consisting of thematic prompts from the facilitator followed by open time for reaction and engagement.
At each workshop we utilized the same data handouts. These included
A 1-page handout with two charts about ice cream consumption in the US: a line chart of per-capita
consumption by year, and results of a small survey about favorite flavors.
A 4-page handout about issues of food security in a local community, based on surveys administered by a
non-profit. This included items such as tables summarizing time spent getting groceries, quotes about
barriers to acquiring appropriate food, and bar charts of population demographics.
A 4-page handout about healthy eating habits from a government-run local early childhood support program.
This included tables of population demographics, bar charts indicating family size and country of origin,
quotes about preferred patterns of eating, and more.
Between sessions we iterated on the activities and incorporated virtual or physical constraints in addition to
participant feedback. In Workshops #1 and #2 we attempted to engage participants as co-designers of the
experience; commenting on, and suggesting ideas for, how to run activities in this domain. This deliberately
reinforced our approach to justice and empowerment, not just in terms of product but also the processes we
undertook. Due to the speculative nature of this work, and our desire to have participant feedback shape next
steps, we hoped this format would allow space for topics and reflections we did not anticipate to emerge.
3.2. Data collection and analysis
Multiple streams of data were collected at each workshop. Each workshop was video recorded and automatically
transcribed to support later evaluation work. During the workshops, members of the research team took on one of
three roles:
Facilitator: the leader responsible for introducing activities, monitoring progress during breakout sessions,
and prompting reflection.
Non-participant observer: a fly on the wall tasked with taking notes about the interactions and reactions of
participants during the session itself, including notable participant statements and reflections.
Participant: worked alongside students and contributed comments on the social dynamics of the session after
the fact.
We held a short debrief after each workshop to share comments and reflections, with collective note-taking
happening in a shared online document.
We conducted evaluation through qualitative review of these outputs. One team member reviewed the video
transcription, observer notes, and debrief notes to synthesize high level findings. This involved looking for
recurring observations based on connections to our three research questions and extracting key quotes from
participants that were marked or mentioned by researchers in any of the notes taken. We took a hybrid approach
to doing this qualitative descriptive coding, based on both our primary research questions and topics that
emerged within the transcriptions and notes themselves. We deemed this appropriate because while we do have
some key research questions, this work was also highly exploratory in nature. Findings from this qualitative data
analysis were brought to the larger research team for reflection and further discussion.
3.3. Workshop #1: Exploring movement and data literacy
Our first workshop was an exploration of movement and data designed to surface initial reactions and reflections
(Table 1). The driving research question for this workshop was RQ1 - Literacy. We structured it to evaluate what
kind of basic data literacy skills could be explored via movement and embodiment.
3.3.1. Activities
The two central activities were (1) a puppeting experience and (2) a collaborative design of a short
performance in small teams.
Activity #1 Puppeting: This activity was inspired by image theatre (see Section 2.2), where participants
sculpt bodies to embody perspectives, and augmented by movement cards (Figure 1) we developed through a
This content downloaded from
202.120.224.68 on Wed, 29 Nov 2023 02:34:33 +00:00
All use subject to https://about.jstor.org/terms
99
preliminary review of movement research. In this activity, each group of four received a movement card (Figure
1) and a dataset (Figure 2). Each pair was invited to review the data handout, about ice cream consumption in the
US (Figure 3), and then to instruct the other pair on positioning their bodies to express the data, constraining
their actions to the movement prompt card they received. Then the roles switched.
Figure 1. A sample of the movement cards provided to participants
Figure 2. The ice-cream data handout
Activity #2 Collaborative design of a short performance: Each group received one of two multi-page
qualitative and quantitative data handouts; one focused on local food security data, the other on a local program
around healthy eating. The groups were instructed to review and discuss the data handout and develop a series of
movements to represent a story that they saw within it. After about 15 minutes of work time, the groups were re-
engaged to share their performances (if they wished to).
This content downloaded from
202.120.224.68 on Wed, 29 Nov 2023 02:34:33 +00:00
All use subject to https://about.jstor.org/terms
100
Figure 3. Participants reviewing data and planning their performance
3.3.2. Observations and reflections
Some of the pieces created included:
Puppeting bodies to mimic the curve of a line chart showing ice cream consumption over time.
Puppeting peers to mime ice cream being scooped and served based on select rates of consumption.
A performance representing hunger via painful expressions and doubling over, and highlighting barriers to
food access by performing the wait at a bus stop.
The overall workshop was both playful and reflective. We were successfully able to create an atmosphere of just
trying things out; as seen in comments about we were having fun,” that was really fun,” and all that data stuff
was a lot of fun.” The last one is particularly telling for us, because it suggests that the participant did not expect
working with data to be fun.
Specifically related to RQ1 - Literacy, we saw initial evidence that participants were engaging with some core
areas of data literacy: encodings, representation, and editorial choices in storytelling.
The two puppeting examples shared above indicate that participants were exploring a variety of modes for
representing data in physical form. The first group commented that they were recreating the chart through
bodily movements like a one-to-one translation. This suggests to us that this team was exploring how to bring
the existing 2D representation into a 3D space with their bodies. Perhaps their meaning-making through
mimicking the chart might be like learning to read a graph? On the other hand, the second example, miming ice
cream, to us was akin to using symbols to represent data, as in pictographs. They were using the literal act
captured in the data, eating ice cream, to represent to the audience a story they saw in the data. This reflects
evidence from other work where we found early novice data literacy learners often draw symbolic depictions to
represent data (Bhargava et al., 2021). Taken together, these two examples show participants engaged in
explorations of body-based representation that brings them into contact with questions asked in more traditional
data literacy introductions. They stated how the intention to to use the movements to explain the data,” and
used different movements for different quantities. The visual encoding space of traditional charts is well-
document and explored, but the encoding and representations possible via embodiment merit further
investigation.
We were also provoked into questions about predisposed notions of analysis and embodiment being separate
tasks. One participant noted its really weird to think about how to turn numbers into something... having no
scales changes things. This offers a potentially interesting hypothesis that relates to prior work reviewed on
education and embodiment (Lakoff & Johnson, 1999; Höök, 2018; DIgnazio & Klein, 2020), specifically that
non-graph-like embodied ways of understanding were being dismissed by participants even when prompted to
create embodied representations of knowledge. Interestingly, one participant went further and pointed out that
the limits of embodiment also mattered - what are we trying to do… what can we not do physically? Relatedly,
another commented that it was really interesting to think about how you make scales different based on the type
This content downloaded from
202.120.224.68 on Wed, 29 Nov 2023 02:34:33 +00:00
All use subject to https://about.jstor.org/terms
101
of visualization; perhaps demonstrating an ability to reflect on chart design decisions after the physical act of
embodying the data.
The performance activity showed participants wrestling with questions of turning a data into a story. One noted
that it is really hard to represent all of the data,” a reflection related to questions of what data is included in a
story. When prompted, one participant more directly commented that I found the hardest part was to create a bit
of a narrative there… how do we take the different parts of the data and make a story out of it. One participant
in response commented that even with the same graph, we interpret - perhaps pointing to an early
understanding that any data can contain a multitude of stories. Another pointed out that as an audience member
you don’t have a context for the data… youre almost looking more closely, trying to process every movement.
Storytelling with data is a difficult skill, but these comments suggest that the performance activity in particular
was leading participants to wrestle with it. This provides early evidence that our two activities were helping
participants explore some fundamental pieces of data literacy - specifically how to represent data and turn it into
a story.
On a final note, we found most people focused on acting out stories from the data as skits, while a few translated
the data into more abstract dance-like movement. Their skits were very narrative in their structure, while the
dance seemed to be evoking emotions they saw in the data. This drove a shift in our project structure, offering
two paths: (1) diving into data and dance (publication forthcoming), and (2) exploring data and theatre (described
in this paper).
3.4. Workshop #2: Prototype data theatre
We specifically designed the next workshop to use practices of participatory, socially engaged theatre to build
data literacy in a group of participants that did not see themselves as speaking data.” We built on a Brechtian
approach to theatrical practice and invited participants in as co-designers of the activities; after each activity we
asked them to take off their participant hat and comment on the design of the activity itself with their
facilitator/educator hat.” Building on what we observed at the first exploratory workshop, and our learnings
from the field of participatory theatre, our driving research question for this workshop was RQ2 - Critique.
3.4.1. Activities
The two central activities were (1) a collective puppeting experience and (2) creating short data skits in small
groups.
Activity #1 Collective Puppeting: Here we extended the puppeting activity to the group at large - pairs of
participants designed ways to showcase something they saw in the ice-cream data by instructing the rest of us
about what to do with our bodies. This connected to our goal of breaking down the divide between audience and
performers - inviting the entire audience to make meaning by performing a piece. The reflection discussion
afterward focused on questions more targeted at critical thinking.
Activity #2 Data Skits: After discussing what makes up a story (versus just raw data) we introduced the idea
of finding a story and designing a 23-minute skit that tells that story. To dig into RQ3 - Impact a bit more we
asked them to focus on conveying one key emotion to their audience. They were given about 15 minutes for this
activity and invited to perform their piece after that.
3.4.2. Observations and reflections
Some of the pieces created included:
Puppeting the group to compose a line chart with their arms across a grid of video boxes in Zoom.
Puppeting one participant at a time to represent the popularity of a flavor of ice cream by trying to represent
it via movement and sound.
A skit comparing time spent getting to a grocery store by different populations via pretending to drive a car.
A skit acting out a debate between two fictional community members about whether the data about cultural
eating norms was reductive, stereotypical and offensive.
This content downloaded from
202.120.224.68 on Wed, 29 Nov 2023 02:34:33 +00:00
All use subject to https://about.jstor.org/terms
102
Overall, we were pleasantly surprised at the engagement from participants despite the constraints of the virtual
setting. We hypothesize that leading silly introductory activities within the boxes on Zoom created a sense of
failure and awkwardness being a normal part of the process, freeing participants to be slightly more uninhibited
in their ideas. While reflecting on this first activity, some participants shared that they were a little lost at first,
another commented that once we got into it, it was really really fun. Again, this reinforced a finding from
Workshop #1, specifically that embodying data via our activities was a fun process.
The car driving skit provided an evocative example of using theatrics to create space for questioning in the
audience (similar to the A Sort of Joy piece described in 2.3). The awkward silence of the final driver continuing
to drive for far too long created space to question why their drive was so much longer.
A number of comments offered early evidence that we are creating a new pathway for learners who perceive
themselves as non-numbers people. An incidental conversation as the workshop was starting led to many
participants self-identifying as non-numbers people. This binary identity formation in relation to math creates a
real barrier to entering domains that learners assess as numbers-heavy (Leonard et al., 2020). In future
workshops we would more robustly capture this in a pre-survey, providing an informal qualitative indication of
how the group felt. In addition, we could embrace Leonards notion of naming discomfort as a potential
intentional response to try break down any perceived barriers felt by participants.
The debate skit mentioned above provided our strongest example of participants engaging in critical questioning
of datasets and their use (as described in RQ2 Critique). They challenged the assumptions they saw in the data.
Based on our decade of experience running data literacy activities we found this quote notable - critiquing the
data like this rarely happens with novices. The fact that this group did just that suggests that we were able to
create an environment where they felt comfortable engaging their critical thinking skills despite the power
dynamics at play within the workshop. In addition, some of this early evidence related to our commentary on
control of impact (section 1.1). Our choice to use local data resonated with a number of the participants. One
noted that our approach allows communities to redefine what is a hard-tangible fact with emotions and lets
the audience have freedom to take from it what they are going to take from it. These examples suggest that our
Freire-inspired approach to designing with community data with impact might have successfully introduced the
basics of the critical data literacy concept introduced previously.
At the process level, one helpful comment suggested that calling them data skits was inappropriate; the term
implies humor, and this was not engaging in humorous approaches.
3.5. Workshop #3: In-class data theatre
Our third workshop was a single session in one of the authors in-person Movement course for undergraduate
theatre majors. As with workshop #2, our main goals revolved around testing the activities to determine how
they support helping a new group of participants start to move from data to story through the design of
participatory performances.
3.5.1. Activities
The two central activities were (1) a prompt to embody a dataset and (2) a prompt to make a (data) scene.”
Activity #1 Embody a Dataset: We did not substantially alter this activity.
Activity #2 Make a (Data) Scene: Building on our learning from Ellinger of True Story Theatre (see 2.3), we
added a bit more intentionality in how we introduced the data. Using the same handouts, we took more time to
address the fact that they might connect to negative experiences of people in the room and we attempted to create
space to honor and digest that. Additionally, we began using the term scene in place of skit.”
3.5.2. Observations and reflections
Some of the pieces created included:
Embodying data about ice cream consumption over time by directing the class to move around the space and
hum, while varying volume and speed based on the data instructions.
Embodying data about consumption levels via standing at different heights.
This content downloaded from
202.120.224.68 on Wed, 29 Nov 2023 02:34:33 +00:00
All use subject to https://about.jstor.org/terms
103
A scene making physical all the obstacles that a disadvantaged person faces in accessing food for her family,
from time, to distance, to money, in a journey to feed her children.
A dramatic scene where a character was taunted with healthy food and other resources that were too high
above her head or too fast for her to catch.
The embody activity offered two interesting notes. The first related to sound and movement - this was the first
intentional use of encoding via sound that we saw. We found it encouraging that these activities led participants
to a mode of data representation that has only recently gained in popularity - sonification (Lenzi & Ciuccarelli,
2020). The second relates to binning - the process in statistics of grouping a set of discrete data into larger
categories. In this specific case, the team quickly averaged ice cream consumption levels by decade to instruct
participants how to stand. In more traditional data literacy programs, we often see learners introduced to the idea
of binning and how it can alter the results of an analysis (based on bin size). Here we saw the participants come
to the idea themselves and used the opportunity to discuss the idea and concerns during the reflection after they
presented it. These both suggest interesting learnings related to the analysis and representation pieces of data
literacy.
Regarding RQ3 - Impact, we saw early indications of participants reflecting on the subjects of the data in
meaningful ways. One group noted that the make a scene activity helped them get around the dehumanization
that can occur in traditional representations, saying specifically that numbers / statistics are not just numbers,
they represent people going through this. A viewer of that scene said it pulled me in because it matched
something that happened to me in my life. These comments help us dig into some of the driving questions
related to how performing a dataset could change perspectives on those represented by the data, and potentially
build empathy for their situation. Furthermore, one participant started to get at a potentially causal link that
merits more investigation, stating that their emotional response to the data was a lot stronger when we
embodied the data.” However, another commented that one weakness is how the paper visual representation is
stronger because you can reflect back and look at it it is locked in time.” These suggest the participants were
engaged in the act of embodying the data and were reflecting on its affordances; there may be aspects such as
recall that are not supported through embodied representation.
3.6. Synthesizing two activities
Based on our interaction and reflections from the 3 separate workshops we synthesized our learnings into two
data theater learning activities for novice audiences. We offer these as the foundation of an approach to data
theatre in service of equity and engagement in learning settings that focus on justice. The Embody a Dataset
and Make a (Data) Scene activities are documented in activity guides for educators attached as supplemental
material to this paper.
4. Synthesis and discussion
In this section we highlight findings and reflections across workshops and research questions. In particular, we
synthesize and discuss our initial findings in relation to each of our guiding research questions. With our focus
on undergraduate theatre students as primary participants, findings may not be generalizable to other audiences
we plan to work with, such as community organizations. However, the findings do suggest an initial foundation
on which to build and are applicable for others wishing to work with this same or related audiences. Our early
observations suggest that data theatre is a novel entry point into data literacy, worthy of more study (as detailed
in 5 below).
4.1. Creating many paths for many learners (RQ1 - Literacy)
Our initial workshops and prototype activities suggest that data theatre can introduce participants to several core
data literacy skills - reading data, picking representations, and creating stories.
Our activities were appealing to audiences that might not be drawn to spreadsheets, math, and the computational
analytic tools typically used in introductory data literacy sessions. Participants associated the term data with
other words such as information,” numbers,” research,” charts,” and technology.” When prompted to
represent data with a sound of movement, we heard computational bleeps, bloops, and mechanized motions. In
addition, we found in Workshops #2 and #3 that our participants did not identify as math people (Leonard et
This content downloaded from
202.120.224.68 on Wed, 29 Nov 2023 02:34:33 +00:00
All use subject to https://about.jstor.org/terms
104
al., 2020). One participant noted, You are translating your math mind into a creative mind, into a story mind,
into a people mind. Another participant echoed that (we were) looking at a piece of data and the numbers and
quantitative stuff, but whats the story here. A third said that I think that is part of finding the stories, you have
to translate. These comments suggest that participants were building basic data literacy skills, specifically in the
realms of representation and storytelling. Our responses could employ Cribbs proposed pathway from
performance to recognition to mathematics identify more intentionally in response to these types of
comments about engaging different parts of ones brain (Cribbs et al., 2015). Combined, these observations
deepen our motivation to create pathways into data literacy for learners who are not attracted to the existing
computation-centered introductions, specifically related to our concern about citizens abilities to join in civic
discussions that are increasingly data-centered.
This returns to our goal of validating multiple ways of knowing in the field of data science. Specifically informed
by the concept of epistemological pluralism (Turkle & Papert, 2015) and data feminism (DIgnazio & Klein,
2020), data theatre can serve as an entry point for learners that take a different approach to thinking through data.
For instance, while many introductions to data begin with aggregating a spreadsheet, we saw many of our
participants begin by trying to understand one data point. Perhaps like the story of Anne that Turkle and Paperts
share, where the bricoleur programmer covers her bird like a painter might, data science needs to embrace
solutions that are informed by approaches from other domains, even if they seem sub-optimal or inefficient
to experts. Our data point explorer should also be welcomed as having a valid way of exploring a dataset.
Another connection here is to the idea of kinesthetic memory as another way of knowing (Lowenfield, 1957).
Participants in our workshops were using their body to process, understand, and express data concepts and
interpretations.
4.2. Building critical data literacy (RQ2 - Critique)
In this context, we see data theatre as a promising approach that could help engage a critical frame of mind about
the data itself, though this work did not engage critique of datas use directly.
It is illuminating to bring this discussion back to Tygel and Kirschs (2015) conceptualization of critical data
literacy. Our participants pointed out that statistics and data sometimes generalize experiences and are not
telling the whole story just by looking at the numbers,” and using reflection and performance shows the story
underneath it that the data attempts to bury. These comments suggest that, as Tygel and Kirsch (2015) define it,
our participants are seeing data as an output of a social process.” In our second workshop, one participant noted
that I was really self-conscious about being critical of the data set, but then they proceeded to despite that
hesitation (creating the data debate described in section 3.4.2). In reference to Tygel and Kirschs (2015)
definition again, we argue this group demonstrated data manipulated based on explicit objectives and including
a social evaluation of what message should be transmitted.” Specifically, we found participants were willing to
interrogate and question data when asked to physically embody it. As one participant noted in the Workshop #3,
you bring your own interpretation or experience to the performance.” A data feminist might suggest you do the
same with data - consider context is the sixth principle of data feminism (DIgnazio & Klein, 2020).
We saw less evidence that we effectively prompted critical reflection about the use of data in social settings as
disempowering or emancipatory. This has become more important to engage as large companies with massive
technical capacity continue to use data-based systems to impact far larger swaths of the population, with more
significant impacts on the historically marginalized (Noble, 2018). If we create new paths to engage populations
in civic data-driven decision making, our definition of justice indicates it must be in service of those with the
most potential to be harmed by the decision. Our prompts were not designed to help participants consider the
impacts of data use in a community.
4.3. Creating impactful embodied experiences (RQ3 - Impact)
These initial workshops lead us to believe that our Make a (Data) Scene activity can rehumanize data and
potentially build empathy and engagement for participants, but we are left with more questions about impacts for
the audience.
One consistent reflection in our work was that the theatrical embodiments of these data points help participants
rehumanize the data. Typical introductions to data are decontextualized from the subjects of the data, and their
points of impact (Oliver & McNeil, 2021), a critique we have made in other work about introductions to data
literacy (DIgnazio & Bhargava, 2015). We argue that with a justice-focused lens it is centrally important to
This content downloaded from
202.120.224.68 on Wed, 29 Nov 2023 02:34:33 +00:00
All use subject to https://about.jstor.org/terms
105
integrate the lived experience of those represented within a dataset, and the context of its potential implications.
Participants reflected that the scene activity grounded it in the actual people and got me thinking about the
people behind the data, behind the numbers. They were telling the story of the people behind the data. This
provides early evidence that embodying data may have helped these learners remember that data often represents
people and experiences. This work allows them to consider their own experiences and knowledge in relation to
the communities the data is about key steps for building empathy.
Furthermore, we heard some reflections suggesting that our activities created impactful reflective experiences for
the participants themselves. For instance, responding to a question about the datasets, one participant noted that it
pulled me in because it matched something that happened to me in my life. This comment reinforces findings
from the field of teaching data for good,” where educators have found that learners get drawn in by working on
datasets about problems they have experienced or are interested in (Bhargava, in press). This also relates to
comments from our interview with Ellinger of True Story Theatre, about creating space to process seeing oneself
in the data engaging the audience in empathetic reasoning.
5. Conclusions and next steps
In this paper we discuss the motivations, inspirations, process, and findings from the creation of a set of data
theatre activities in a higher education setting. We offer the activities as an alternate entryway to building a
critical data literacy, one that builds on processes rooted in questions of justice and equity, decentering
technology and inviting sets of learners who are not engaged through current approaches. We find early evidence
that the data theatre approach is effective at helping participants build some data literacy skills without
introductions to statistics and computational tools, reflect critically on datasets and their intended use, and
engage in emotionally meaningful embodied performance of data stories.
In a datafied world, we argue that democratic societies are morally responsible to govern through understandable
mechanisms. Data theatre offers one path to making data more understandable, particularly for learners without
access to technology, or who do not take to spreadsheets. We hope the activity guides included as appendices
contribute concretely to other educators toolboxes. Theatre based introductions to data literacy can be an
effective entry point into data literacy that complements other approaches and potentially lowers barriers to
certain content based on their embodied nature.
5.1. Next steps
In an educational setting and a community setting, these introductions should serve as tools of empowerment for
populations typically left out of data-centered decision processes. We hope to move beyond the college
classroom in future work, trying these activities out with community groups that work on issues of justice that
involve data. The coronavirus pandemic limited our ability to start with these groups, and in-person workshop
with them would remove the barriers of access to bandwidth and computers for online Zoom sessions.
These initial observations and examples present us with numerous potential next steps focused on exploration,
formalization, and robustness. For instance, we could administer pre- and post-surveys at workshops asking
participants about their confidence levels vis-a-vis working with data. This would ascertain whether a single
experience with these activities creates a short-term impact on self-assessed abilities, and perhaps build an
evidence base for overcoming skills-based identity barriers to working with data. Similarly, to explore the
challenge of building empathy with subjects of data, we could more directly ask learners how connected they
feel to the data and the performance they created.
Particularly related to creating impact on the audience, we believe there is more theoretical grounding from
theatrical practice to explore. One path forward would connect more directly to the concept of kinesthetic
empathy - the idea that audiences experience empathy by watching a performance (Martin, 1975; Reynolds &
Reason, 2012). Another relates to further exploration of feminist theatre practice and the idea of creating
spectacle via performance in public place. The work of the Womens Street Theatre Group at the 1970 Miss
World competition is an evocative example (Cowley et al., 1971). In addition, we could engage more directly
with the Brechtian dialectical theatre approach of interrupting performance to engage the audience. A
participant noted we could think about not only how it can abstract from a situation but also how you can re-
invite into that situation.
This content downloaded from
202.120.224.68 on Wed, 29 Nov 2023 02:34:33 +00:00
All use subject to https://about.jstor.org/terms
106
References
Abrahamson, D., & Lindgren, R. (2014). Embodiment and embodied design. In The Cambridge handbook of the learning
sciences (2nd ed., pp. 358-376). Cambridge University Press. https://doi.org/10.1017/CBO9781139519526.022
Barab, S. A., Cherkes-Julkowski, M., Swenson, R., Garrett, S., Shaw, R. E., & Young, M. (1999). Principles of self-
organization: Learning as participation in Autocatakinetic systems. Journal of the Learning Sciences, 8(3-4), 349-390.
https://doi.org/10.1080/10508406.1999.9672074
Bhargava, R. (in press). Teaching data that matters: History and practice. In J. E. Raffaghelli (Ed.), Data Cultures in Higher
Education: Emergent Practices and the Challenge Ahead. Springer.
Bhargava, R., & DIgnazio, C. (2015, April). Designing tools and activities for data literacy learners [Paper presentation].
Data Literacy Workshop at WebScience 2015, Oxford, UK.
https://voragine.net/img/2021/02/DataLiteracyRahulCatherine.pdf
Bhargava, R., & DIgnazio, C. (2017, June). Data sculptures as a playful and low-tech introduction to working with data
[Paper presentation]. Designing Interactive Systems, Edinburgh, Scotland. https://hdl.handle.net/1721.1/123453
Bhargava, R., Kadouaki, R., Bhargava, E., Castro, G., & DIgnazio, C. (2016). Data murals: Using the arts to build data
literacy. The Journal of Community Informatics, 12(3), 197-216. https://doi.org/10.15353/joci.v12i3.3285
Bhargava, R., Williams, D., & DIgnazio, C. (2021). How learners sketch data stories. In 2021 IEEE Visualization
Conference (VIS) (pp. 196-200). IEEE. https://doi.org/10.1109/VIS49827.2021.9623299
Blair, D., DIgnazio, C., & Warren, J. (2014). Less is more: The Role of small data in 21st century governance. Public Lab.
https://publiclab.org/n/11421
Boston Transportation Department. (2017). Go Boston 2030: Vision and action plan.
Boal, A. (1993). Theatre of the oppressed (C. A. McBride, Trans.). Theatre Communications Group.
Bourdieu, P., & Passeron, J.-C. (1990). Reproduction in education, society and culture (R. Nice, Trans.; 2nd ed.). Sage
Publications.
boyd, d., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly
phenomenon. Information, Communication & Society, 15(5), 662679. https://doi.org/10.1080/1369118X.2012.678878
Carretero, G. S., Vuorikari, R., & Punie, Y. (2017). DigComp 2.1: The Digital competence framework for citizens (EUR
28558 EN). Publications Office of the European Union. http://svwo.be/sites/default/files/DigComp%202.1.pdf
Catchesides, K. (2020). Verbatim theatre. Stockroom. https://www.stockroom.co.uk/old-pages/verbatim-theatre/
Couldry, N., & Mejias, U. (2019). The Costs of connection: How data is colonizing human life and appropriating it for
capitalism. Stanford University Press.
Cowley, S., Filner, C., Golditch, R., Hope, E., Howard, P., Hoyland, W., Lamb, K., Linebaugh, B., Melamed, A., Reid, T.,
Layla, & Simpson, A. (1971, March). News and notes. Shrew: Womens liberation workshop, 3(2).
Cribbs, J. D., Hazari, Z., Sonnert, G., & Sadler, P. M. (2015). Establishing an explanatory model for mathematics identity.
Child Development, 86(4), 1048-1062. https://doi.org/10.1111/cdev.12363
Data Literacy. (n.d.) Queensland curriculum and assessment authority. https://www.qcaa.qld.edu.au/p-10/aciq/frequently-
used-resources/data-literacy
de la Croix, A., Rose, C., Wildig, E., & Willson, S. (2011). Arts-based learning in medical education: The Students
perspective. Medical Education, 45(11), 1090-1100. https://doi.org/10.1111/j.1365-2923.2011.04060.x
Dewey, J. (1903). Democracy in education. The Elementary School Teacher, 4(4), 193-204.
DIgnazio, C., & Bhargava, R. (2015). Approaches to building big data literacy. Bloomberg data for Good Exchange 2015.
http://www.kanarinka.com/wp-content/uploads/2021/01/DIgnazio-and-Bhargava-Approaches-to-Building-Big-Data-
Literacy.pdf
DIgnazio, C., & Klein, L. F. (2020). Data feminism. The MIT Press.
Dirnstorfer, A., & Saud, N. B. (2020). A Stage for the unknown? Reconciling postwar communities through theatre-
facilitated dialogue. International Journal of Transitional Justice, 14(1), 122-141. https://doi.org/10.1093/ijtj/ijz038
Edelbi, K. (2020). Using Playback theater with adolescents in refugee camps in Palestine to tell their stories (Publication No.
27833283) [Doctoral dissertation, Lesley University]. ProQuest Dissertations and Theses Global.
Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martins Press.
Farmer, D. (2014). Image theatre. Drama Resource. https://dramaresource.com/image-theatre/
This content downloaded from
202.120.224.68 on Wed, 29 Nov 2023 02:34:33 +00:00
All use subject to https://about.jstor.org/terms
107
Flender, S. (2019). Data is not the new oil. Towards Data Science. https://towardsdatascience.com/data-is-not-the-new-oil-
bdb31f61bc2d
Fragapane, F. (2017, April 26). Data visualization and theatre: A Story of mutualism. Medium.
https://medium.com/@frcfr/data-visualization-and-theatre-a-story-of-mutualism-5e199009cf4b
Freire, P. (1968). Pedagogy of the oppressed. Routledge.
Gould, R. (2017). Data literacy is statistical literacy. Statistics Education Research Journal, 16(1), 2225.
Gray, R. E., & Sinding, C. (2002). Standing ovation: Performing social science research about cancer. AltaMira Press.
Gutierrez, G. (1988). A Theology of liberation: History, politics, and salvation (C. Inda & J. Eagleson, Trans.; Revised ed.).
Orbis Books.
Gutiérrez, M. (2018). Data activism and social change. Palgrave Pivot. https://doi.org/10.1007/978-3-319-78319-2
Hally, T., & Sinha, K. (2018). SHINE for girls: Innovating STEM curriculum with dance. Childhood Education, 94(2), 43-
46. https://doi.org/10.1080/00094056.2018.1451689
Harouni, H. (2015). Toward a political economy of mathematics education. Harvard Educational Review, 85, 50-74.
https://doi.org/10.17763/haer.85.1.2q580625188983p6
Henderson, J., & Corry, M. (2020). Data literacy training and use for educational professionals. Journal of Research in
Innovative Teaching & Learning, 14(2), 232-244. https://doi.org/10.1108/JRIT-11-2019-0074
Höök, K. (2018). Designing with the body: Somaesthetic interaction design. MIT Press.
Jackson, L. A., Zhao, Y., Kolenic, A., Fitzgerald, H. E., Harold, R., & Von Eye, A. (2008). Race, gender, and information
technology use: The New digital divide. CyberPsychology & Behavior, 11(4), 437-442.
https://doi.org/10.1089/cpb.2007.0157
Katuli, M. (2019, January 2). Young artists use fashion and data to promote dialog on sexual health. Data Zetu.
https://medium.com/data-zetu/young-artists-use-fashion-and-data-to-promote-dialog-on-sexual-health-517429662ec2
Klenke, C. M., Schultz, T. A., Tokarz, R. E., & Azadbakht, E. S. (2020). Curriculum data deep dive: Identifying data
literacies in the disciplines. Journal of EScience Librarianship, 9(1), e1169. https://doi.org/10.7191/jeslib.2020.1169
Knaus, T. (2020). Technology criticism and data literacy: The Case for an augmented understanding of media literacy.
Journal of Media Literacy Education, 12(3), 6-16. https://doi.org/10.23860/JMLE-2020-12-3-2
Lakoff, G., & Johnson, M. (1999). Philosophy in the flesh: The Embodied mind and its challenge to western thought. Basic
Books.
Leonard, A. E., Bannister, N. A., & DSouza, N. F. (2020). (Non)dance and (non)math people: Challenging binary
disciplinary identities in education. Research in Dance Education, 0(0), 1-19.
https://doi.org/10.1080/14647893.2020.1853692
Lenzi, S., & Ciuccarelli, P. (2020). Intentionality and design in the data sonification of social issues. Big Data & Society, 7,
205395172094460. https://doi.org/10.1177/2053951720944603
Leonard, A. E., Bannister, N. A., & D’Souza, N. F. (2020). '(Non)dance and (non)math people’: Challenging binary
disciplinary identities in education. Research in Dance Education, 119. https://doi.org/10.1080/14647893.2020.1853692
Lowenfeld, V. (1957). Creative and mental growth (3rd ed.). Macmillan.
Malinverni, L., & Pares, N. (2014). Learning of abstract concepts through full-body interaction: A Systematic review.
Educational Technology & Society, 17(4), 100-116.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Hung-Byers, A. (2011). Big data: The Next
frontier for innovation, competition, and productivity. McKinsey Global Institute.
Martin, J. (1975). Introduction to the dance (New edition of 1939 edition). Dance Horizons Republication.
Moran, G. S., & Alon, U. (2011). Playback theatre and recovery in mental health: Preliminary evidence. The Arts in
Psychotherapy, 38(5), 318-324. https://doi.org/10.1016/j.aip.2011.09.002
Mumford, M. (2008). Bertolt Brecht. Routledge. https://doi.org/10.4324/9780203000991
National Academy of Sciences, National Academy of Engineering, and Institute of Medicine. (2007). Rising above the
gathering storm: Energizing and employing America for a brighter economic future. The National Academies Press.
https://doi.org/10.17226/11463
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
This content downloaded from
202.120.224.68 on Wed, 29 Nov 2023 02:34:33 +00:00
All use subject to https://about.jstor.org/terms
108
Oliver, J. C., & McNeil, T. (2021). Undergraduate data science degrees emphasize computer science and statistics but fall
short in ethics training and domain-specific context. PeerJ Computer Science, 7, e441. https://doi.org/10.7717/peerj-cs.441
ONeil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
Prado, J. C., & Marzal, M. Á. (2013). Incorporating data literacy into information literacy programs: Core competencies and
contents. Libri, 63(2), 123-134. https://doi.org/10.1515/libri-2013-0010
Papert, S. (1994). The Childrens machine (Reprint edition). Basic Books.
PARCOS. (2020). Using Arts-based methods in science communication. LUT University. https://hackmd.io/@art-based-
methods-guidebook/HJMVIhHFL/%2F2UkwoMKeSyugSm59sF2Iew
Perovich, L. J., Cai, P., Guo, A., Zimmerman, K., Paseman, K., Espinoza Silva, D., & Brody, J. (2020). Data clothing and
Bigbarchart: Designing physical data reports on indoor pollutants for individuals and communities. IEEE Computer Graphics
and Applications, 41(1), 87-98. https://doi.org/10.1109/MCG.2020.3025322
Raffaghelli, J. E. (2020). Is Data literacy a catalyst of social justice? A Response from nine data literacy initiatives in higher
education. Education Sciences, 10, 1-20. https://doi.org/10.3390/educsci10090233
Reynolds, D., & Reason, M. (2012). Kinesthetic empathy in creative and cultural practices. Intellect Books.
Ryan, L., Silver, D., Laramee, R. S., & Ebert, D. (2019). Teaching data visualization as a skill. IEEE Computer Graphics and
Applications, 39(2), 95-103. https://doi.org/10.1109/MCG.2018.2889526
Salas, R., Steele, K., Lin, A., Loe, C., Gauna, L., & Jafar-Nejad, P. (2013). Playback Theatre as a tool to enhance
communication in medical education. Medical Education Online, 18, 22622. https://doi.org/10.3402/meo.v18i0.22622
Sander, I. (2020). What is critical big data literacy and how can it be implemented? Internet Policy Review, 9(2), 1-22.
Scheaffer, R. L., & Jacobbe, T. (2014). Statistics education in the K-12 Schools of the United States: A Brief history. Journal
of Statistics Education, 22(2), 8. https://doi.org/10.1080/10691898.2014.11889705
Solomon, F., Vogelstein, L., Brady, C., Steinberg, R., Thomas, C., Champion, D., Lindberg, L., Enyedy, N., DesPortes, K.,
Payne, W., Bergner, Y., Taylor, E., & Shapiro, R. B. (2021). Embodying STEM: Learning at the intersection of Dance and
STEM. In de Vries, E., Hod, Y., & Ahn, J. (Eds.), Proceedings of the 15th International Conference of the Learning Sciences
- ICLS 2021 (pp. 819-826). International Society of the Learning Sciences.
Sommer, S. R., & Polman, J. L. (2017). Embodied activities as entry points for science data literacy. In CSCL 2017
Proceedings (pp. 849-850). International Society of the Learning Sciences, Inc.
Styan, J. L. (1981). Modern drama in theory and practice: Volume 3, Expressionism and epic theatre. Cambridge University
Press.
Thorp, J. (2015). A Sort of joy. Memo (Random). https://medium.com/memo-random/a-sort-of-joy-1d9d5ff02ac9
Timmermann, C., & Havemann, L. (2020). Critical literacies for a datafied society: Academic development and curriculum
design in higher education. Research in Learning Technology, 28. https://doi.org/10.25304/rlt.v28.2468
Turkle, S., & Papert, S. (2015). Epistemological pluralism: Styles and voices within the computer culture. Signs: Journal of
Women in Culture and Society. https://doi.org/10.1086/494648
Tygel, A., & Kirsch, R. (2015). Contributions of Paulo Freire for a critical data literacy. Proceedings of Web Science 2015
Workshop on Data Literacy (pp. 318-34). http://www.dataliteracy.eita.org.br/wp-content/uploads/2015/02/Contributions-of-
Paulo-Freire-for-a-critical-data-literacy.pdf
UNICEF Rwanda, & National Institute of Statistics Rwanda. (2016). Teachers discussion guide for reading data with
children. UNICEF. https://www.unicef.org/rwanda/reports/teachers-discussion-guide-reading-data-children
Williams, S. (2020). Data action: Using data for public good. The MIT Press.
Willett, W., Jansen, Y., & Dragicevic, P. (2017). Embedded data representations. IEEE Transactions on Visualization and
Computer Graphics, 23(1), 461-470. https://doi.org/10.1109/TVCG.2016.2598608
Yap, A. C. (2016). How to teach kids empathy through dance. The Atlantic.
https://www.theatlantic.com/education/archive/2016/01/learning-empathy-through-dance/426498/
Zeidler, D. L., Sadler, T. D., Simmons, M. L., & Howes, E. V. (2005). Beyond STS: A Research-based framework for
socioscientific issues education. Science Education, 89(3), 357-377. https://doi.org/10.1002/sce
This content downloaded from
202.120.224.68 on Wed, 29 Nov 2023 02:34:33 +00:00
All use subject to https://about.jstor.org/terms